The invention relates to the field of computer-aided design. More precisely, it concerns a computer-implemented method for defining seams of a virtual garment or furniture upholstery comprising a plurality of two-dimensional patterns assembled by their edges. It also concerns a computer-aided design system, computer program product and a non-volatile computer-readable data-storage medium containing computer-executable instructions to cause a computer system to carry out such a method, as well as a method of manufacturing a real garment.
In the following, the word “avatar” will be used broadly to designate a computer-generated, usually three-dimensional, representation of a human or animal body, a humanoid or zoomorphic creature, or even a vegetable or an inanimate object (e.g. a piece of furniture such as a sofa). Most often, however, the avatar will be a representation of a human or humanoid body. The avatar may be designed ab initio, or it may be reconstructed by scanning a real body or object.
The words “virtual garment” or “virtual upholstery” will refer to a computer-generated bi-dimensional or (most often) three-dimensional representation of clothing, upholstery or the like, suitable to be worn by an avatar. The virtual garment may be a model of a “real” garment suitable to be manufactured in the physical world. In the following, only the case of garments will be discussed, but all equally applies to upholstery.
Avatars and virtual garments are preferably “three-dimensional” (3D). Hereafter, a “three-dimensional” object will be an object—or a digital model thereof—allowing a three-dimensional representation, which allows the viewing of the parts from all angles.
The word “pattern” will designate a piece of fabric, leather or other flexible material suitable to be used to manufacture a garment. A garment is most often manufactured by assembling several patterns by their edges. Patterns are usually considered two-dimensional, as they are developable (they can lie flat on a plane) and their thickness is negligible (smaller by at least two orders of magnitude) over their other dimensions.
A “seam” is a junction between two edges of two different patterns, or of the same pattern. Typically, the word “seam” used alone will not refer to an “internal seam” which is a junction between two edges of two different patterns or of a same pattern corresponding to an elementary pattern construction and associated to styling elements such as darts, pleats, iron lines, pockets, notches . . . Seams are most often realized by sewing or stitching, but other techniques such as gluing may be used in some cases.
Computer-aided techniques have been widely used in the development process of pattern-making in the fashion industry. Specifically, CAD (Computer Aided Design) and CAM (Computer Aided Manufacturing) systems have helped to produce digital 2D patterns which are then used to manufacture garments. These patterns are generally described as two-dimensional boundary curves enriched with additional information needed to physically construct the final garment. Several CAD systems exist on the market from companies such as Lectra Systèmes, Gerber Technology, Optitex, Human Solutions Assyst, Co3D (Marvelous Designer). The modules they propose present common characteristics and are mainly focused on 2D patterns development, CAD-CAM management and manufacturing (e.g. pattern layout and cut with automatic machine).
With the emergence of 3D, virtual clothing is becoming a standard and it requires new techniques to assemble virtually the 2D patterns in order to get the virtual garment. Unfortunately, the 2D patterns created with standard CAD systems lack information about how to assemble and sew them efficiently. In fact, most of the existing 2D CAD pattern models are conceived for design or cutting of unassembled textile parts. Thus, they do not provide assembly and finishing instructions or such information is not well defined for an automatic manufacturing. Generally, to get the full sequence of sewing, human tailors have to rely on their experience and understanding of the conventions of sewing patterns in the real word.
FIG. 1 shows the general pipeline of a 3D virtual garment prototyping method.
First of all, a set of 2D patterns P of the virtual garment and an avatar AV are provided. As it has been already mentioned, 2D CAD patterns may come from standard industry pattern-marker software; in some cases, they may be obtained by scanning and digitally processing “physical” patterns made of paper. The 3D avatar is created by an artist, generated from anthropometrical survey analysis or obtained from body measurements (e.g. 3D scanning). Then the patterns are positioned around the avatar and assembled using seam definitions. Finally, physical simulation is performed to drape the assembled garment G.
Pattern positioning and seams definition are crucial since they define the starting state for the garment simulation; if they are not provided or not well defined, the simulation will fail. Many research works have been performed concerning the automatic pre-positioning of patterns (see References 1-4 cited later) and proposed by some commercial software like Vidya from Human Solutions. But little progress has been made concerning automatic seam definition, and today 3D design garment modules require that the user manually specifies which edge has to be sewn to another one.
To alleviate this problem of automatic sewing definition there are two approaches that have been proposed by the research community.
One is based on a sketching approach (see Reference 6) which directly generates the 3D geometry of the garment on a 3D avatar. Designers simply draw curves on 3D meshes to create seams. This approach is limited since it cannot produce complex garments and the 3D geometries generated are not guaranteed to be developable, which does not allow to get pattern pieces for the garment manufacturing.
The second method (see Reference 5) tries to parse sewing patterns in PDF (Portable Document Format) format and to determine how the panels must be stitched together based on machine learning and integer programming. This method successfully extracts 68% of the sewing patterns and need users corrections. Such performances are insufficient for most applications.